Quadruped Robot Navigation

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21 papers8 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Oct 13, 2026

DR-VLA: Learning Human-Like Quadruped Navigation from Egocentric Web Videos via Domain-Invariant Scene Representations

Quadruped robots performing outdoor deliveries must interpret complex scenes, anticipate changes, and navigate in socially compliant ways. Robot demonstrations are costly to collect at the scale needed for generalization. Egocentric web videos offer abundant human navigation experience, but differences in viewpoint and motion make this experience difficult to transfer to robots. We introduce DR-VLA, a Mixture-of-Transformers framework that transfers human navigation experience through a domain-invariant scene representation (DISR). The framework combines vision-language understanding and latent world modeling with a flow-matching action expert. We introduce EgoLynx, comprising 10,000 hours of curated egocentric web video (EgoLynx-Web) and 2,000 clips of teleoperated robot navigation collected during real-world deliveries (EgoLynx-Robot). We pretrain the understanding and world experts on EgoLynx-Web without action labels, then adapt the model to robot observations and train the action expert on EgoLynx-Robot to predict executable trajectories. We also propose the Quadruped Navigation Score (QNS), a pseudo-closed-loop metric combining safety, instruction compliance, comfort, and progress. On the held-out EgoLynx-Robot test split, DR-VLA improves QNS by approximately 5% and reduces open-loop final heading error (FHE) by 14.6%, each relative to the best competing result for that metric. Ablations show that web-video pretraining improves generalization and that the model's key components contribute to performance. As a future-prediction target, DISR outperforms five generic visual representations and yields performance close to task-specific 3D perception without dedicated geometric labels or point clouds for target construction. Trained separately on NAVSIM v1 without web pretraining, DR-VLA achieves 92.1 PDMS, suggesting that the framework also applies to autonomous driving.

Jun Tian, Yingjie Niu, Ruo Chen +2

Deep Robotics · University of Illinois at Urbana-Champaign · Zhejiang University +2

Quadruped Robot Navigation Robot Navigation Cross-Embodiment Robot Learning VLMs for Robotics Data-Efficient Robot Learning World Models for Robotics+1

Oct 11, 2026

PointWalker: Mapless Quadruped Locomotion Directly From Raw LiDAR Point Clouds

Quadruped robots deployed for inspection and field missions must cross heterogeneous terrain, from stairs, slopes, and gaps to high platforms, stepping stones, and balance beams, where safe support is sparse or see-through. Existing methods interpose a terrain map or a depth image between sensing and control. Maps are costly to maintain, corrupted by odometry drift and outliers, and misled by see-through gratings, while depth cameras degrade with lighting and surface material. In this paper, we present PointWalker, a mapless framework whose single reinforcement learning policy directly consumes raw point clouds from four light detection and ranging (LiDAR) sensors. Voxelized points are encoded by linear self-attention and fused with proprioception through cross-attention, so terrain geometry reaches the policy without maps or foothold estimation. One proximal policy optimization (PPO) run, with terrain-aware rewards and dual-channel simulation of see-through gratings, yields one policy for all seven terrain classes with a 90.1% mean success rate in simulation. Deployed zero-shot on a Deep Robotics X30 at 50 Hz onboard, it crosses 0.8-m gaps, climbs 0.8-m platforms and 45° steel-grating stairs, carries a 50-kg payload, and completes a 2.5-km mountain route.

Sicen Li, Zhen Chu, Chao Li +3

College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · Yunshenchu Technology Co., Ltd., Hangzhou, China · Hangzhou Public Library, Hangzhou, China +1

Quadruped Locomotion Legged Robotics Terrain-Aware Robot Locomotion Quadruped Robot Navigation RL for Legged Locomotion Point Cloud Learning

Oct 10, 2026

Load-Aware Quadruped Navigation with Online Contact-based Geometry Estimation

Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots. Compared with works that focused on load-aware locomotion adaptation to maintain gait stability, existing autonomous navigation systems generally ignore the influence of carried objects' geometries and use fixed footprint inflation, leading to overly conservative navigation. To fill this knowledge gap, we propose a two-phase load-aware navigation learning framework. In Phase 1, we train LOADNav, a load-aware navigation policy via reinforcement learning, enabling the robot to adapt its trajectory and clearance behavior according to the geometric size of the carried object. In Phase 2, we train CoGE, an online geometry predictor via imitation learning, which estimates unknown payload geometry from occasional contact events. We evaluate the framework in cluttered environments with loads of varying sizes and shapes. The results show that our navigation policy exhibits clear load-aware behaviors, and the online geometry predictor estimates object geometry from occasional contact events, refining its estimate and adapting the navigation behavior accordingly. Without requiring any prior knowledge of the payload, it narrows the gap to geometry-aware policies.

Qi Yin, XiaoLi Zhang, Yangming Shi

Colorado School of Mines, Golden, CO 80401, USA

Robot Navigation Quadruped Robot Navigation RL for Robotics

Oct 7, 2026

CMP-IRRT*: A Perception-Assisted Height-Adaptive Planner for Quadruped Robots

Quadruped robots can traverse low obstacles, but many 2D planning pipelines still model obstacles as binary occupied regions and rely on sampling-based search that can be inefficient under a limited budget. We propose a perception-assisted height-adaptive planning framework based on CMP-IRRT*, a Channel Mamba PointNet-guided Informed RRT* planner. Given a calibrated top-view RGB observation, the perception module estimates obstacle regions and converts depth predictions into a ground-relative height map. The planner then performs height-conditioned collision checking, treating high obstacles as blocked while allowing low obstacles to be traversed, and uses the CMP guide to bias sampling toward promising regions while retaining standard free-space and informed sampling fallbacks. Experiments on 2D planning benchmarks show that CMP-IRRT* reduces explored nodes and iterations compared with classical and neural-guided baselines, and a controlled ablation supports the contribution of the Mamba-based guide. In constructed traversability-aware scenarios, the proposed planner reduces path length by up to 16.3% when low obstacles are traversable, and a Unitree Go2 demonstration further shows executable bypassing and traversal behaviors. Our code is publicly available at https://github.com/MingfanZhao/height-adaptive-planner.

Mingfan Zhao, Wendong Mao, Zhongfeng Wang

School of Integrated Circuits, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China

Quadruped Robot Navigation Robot Navigation Motion Planning Sampling-Based Motion Planning

Oct 2, 2026

TACET: Context-Appropriate Acoustic-Social Navigation for Quadrupeds

Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move: a legged robot's locomotion noise, dominated by foot-ground impacts, is itself a social variable. Prior social navigation respects human space but treats the robot as acoustically uniform, while quiet-locomotion methods reduce noise to an operator-specified, context-blind level. We present TACET, a context-appropriate acoustic-social navigation method that infers social context from the robot's egocentric view and decides both where it walks and how loudly, coupling a slow fine-tuned vision-language reasoner to a fast reactive controller through a single compact behavior token, <gait, speed, social_cost>. The same token conditions both a social costmap (where to go) and a quiet locomotion policy (how loudly to move), while a structured out-of-view memory keeps recently seen people in the reasoner's context after they leave the camera view. On a real quadruped, context-conditioned locomotion lowers locomotion noise by up to 9.3 dBA at matched speed, and across our scenarios the full method keeps personal-space compliance at 100% with low acoustic intrusion (<=2.9 dBA), jointly improving spatial and acoustic performance in the evaluated scenarios. The project page is available at https://rcilab.khu.ac.kr/tacet/.

Sungsan Park, Young-Sik Shin, Sanghyun Kim

Department of Mechanical Engineering, Kyung Hee University, Yongin, 17104, Republic of Korea. · School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.

Robot Navigation Social Robot Navigation Quadruped Robot Navigation Robot Locomotion Quadruped Locomotion

Sep 30, 2026

Experience-Driven Continual Learning of Terrain Traversability for Quadruped Robots

Safe and efficient quadruped navigation over unfamiliar terrain requires predicting terrain-robot interaction before contact: geometry and visual appearance alone cannot reveal how the robot will slip, load its feet, or expend energy. This paper presents a continual learning pipeline that uses locomotion experience to learn these interaction outcomes from pre-contact images and continually updates the predictions as new contacts are observed. Pre-contact descriptors, produced by a DINOv3 backbone model frozen during training, are mapped to five proprioceptive indicators weighted according to measurement reliability: planar foot slip, mean normal ground-reaction force, traction index, cost of transport, and touchdown loading rate. A compact evidential regressor allows us to predict these indicators together with aleatoric and epistemic uncertainty from the visual descriptors. Continual adaptation combines bounded experience replay with a validation gate: candidate models replace the deployed predictor only when they improve performance on recent held-out data while keeping degradation on historical held-out data within a prescribed tolerance. Predictions and epistemic uncertainty are projected into a local multilayer map and combined into a conservative traversability score map whose property weights can be adjusted without retraining. The resulting map is used for downstream navigation tests. The ROS2 implementation supports evaluation on a Unitree Go2 in simulation and on hardware, with models trained separately in each domain. On a sequential hardware stream over three previously unseen terrains, gated replay reduces final anchor negative log-likelihood (NLL) degradation by 23.1% relative to replay without the gate while attaining similar new-terrain adaptation.

Luca Bricarello, João Carlos Virgolino Soares, Alberto Sanchez-Delgado +2

Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia, Genoa, Italy. · Universit`a degli Studi di Genova, Genoa, Italy.

Continual Robot Learning Safe Robot Navigation Terrain-Aware Robot Locomotion Replay-Based Continual Learning Quadruped Locomotion Quadruped Robot Navigation

Sep 26, 2026

FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation

Moving obstacles can block a previously clear route while a quadruped robot executes a motion command. We investigate whether predicting changing clearance improves navigation when motion selection accounts for the robot footprint and the time needed to react and brake. We present FutureRay, which predicts ranges across viewing directions and future times, together with encounter risk, from depth-derived range history and observable robot motion. Training emphasizes near-term clearance and penalizes errors that overstate available space. A local planner queries the same forecast for candidate headings and combines it with current observations to check clearance around the robot footprint. Model-based reaction--braking limits guide speed selection, and the resulting velocity commands are passed to a fixed locomotion policy. In paired evaluations on 60 static and dynamic simulation scenes, FutureRay achieves 93.3% completion, compared with 75.0% for current-range persistence and 80.0% for Cartesian Kalman rollout, with perception, planning, and locomotion held fixed. FutureRay also records fewer collisions than both baselines. Qualitative trials on a physical quadruped show avoidance initiated while an obstacle is approaching the route, followed by renewed goal progress. These results show that joint range and encounter-risk prediction can improve obstacle avoidance without retraining the locomotion policy.

Tianhao Zang, Shanze Wang, Ziqian Wang +4

College of Information Science and Technology, Eastern Institute of Technology, Ningbo, P. R. China. · University of Maryland, USA. · The Hong Kong Polytechnic University, Hong Kong. +2

Robot Navigation Safe Robot Navigation Quadruped Robot Navigation Dynamic Obstacle Avoidance

Sep 23, 2026

Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots

We present Go2-DrivoR, a goal-conditioned adaptation of the end-to-end autonomous driving trajectory planning framework DrivoR for urban navigation with quadrupedal robots. By conditioning trajectory generation on a local-frame subgoal through a goal token and adapting the vehicle-centric scoring formulation, the method extends DrivoR to short-horizon goal-conditioned local planning without redesigning its core decoders. Specifically, we redefine drivable-area compliance for sidewalk-oriented navigation and reformulate the original ego progress term as goal-conditioned ego progress. Trained exclusively on TartanGround simulation data, Go2-DrivoR improves waypoint-conditioned planning performance on unseen simulation environments and transfers zero-shot to open-loop real-world trajectory prediction.

Joochan Kim, Chanuk Yang, Tackgeun You +2

Purdue University · Korea Institute of Science and Technology (KIST)

Robot Navigation Quadruped Robot Navigation Robot Policy Adaptation End-to-End Autonomous Driving Sim-to-Real Transfer

Sep 15, 2026

LEAP: Learning Emergent Active Perception for Quadruped Navigation

Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment. Learned systems typically encourage this behavior with hand-designed proxy objectives, such as coverage or curiosity bonuses, that may conflict with the task. In this work, we propose a method to learn emergent active perception (LEAP) without augmentation of the task objective. We formulate the problem of goal-oriented navigation over hazardous terrains with goals that must be discovered visually. We then propose an architecture for navigation policies with active perception, and train them on a terrain curriculum where task pressure alone leads to the emergence of gaze control. Key to this emergence, LEAP works on a gaze-invariant representation that integrates depth images into egocentric belief maps. We validate its performance in held-out evaluation scenarios, where it achieves a 92.7% success rate, compared to 74.2% for scripted or 34.5% for passive perception, and comes within 4.6 points of a privileged oracle. We validate that LEAP navigation policies, unchanged, can be directly applied to steering quadrupedal locomotion policies in physics simulation.

Ü. Bora Gökbakan, Stéphane Caron, Philippe Souères

WILLOW · ISIR · LAAS-GEPETTO

Robot Navigation Quadruped Robot Navigation Active View Selection Active Visual Perception Active Vision

Sep 3, 2026

MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains

Quadruped robots have demonstrated impressive agility in parkour locomotion across complex terrains. However, most systems still rely on human intervention for high-level planning, and autonomous parkour navigation remains underexplored. The key challenges include fine-grained velocity regulation, long-horizon anticipatory behaviors, and tight coupling between perception and embodied execution. To address these challenges, we propose a Multimodal Diffusion Policy (MulDP) that integrates visual perception with robot proprioception and goal information to generate temporally coherent and anticipatory navigation velocity commands, tightly coupling perception with embodied control to enable robust autonomous navigation. To support the training of MulDP, we construct the first Quadruped Parkour Navigation Dataset (QPND), a multimodal dataset that encompasses diverse navigation behaviors and complex terrains. Extensive simulation and real-world experiments demonstrate that MulDP enables robust long-horizon autonomous navigation and effective traversal across complex terrains.

Kangmai Hu, Yueqi Zhang, Peng Zhai +5

College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, China

Robot Policy Learning Robot Navigation Diffusion Policy Terrain-Aware Robot Locomotion Quadruped Robot Navigation

Aug 22, 2026

DELTA: Deformable Elevation-Based Local Terrain Attention Encoder for Sparse-Terrain Quadrupedal Locomotion

Stable quadrupedal locomotion on sparse terrain requires selecting state-relevant terrain evidence for precise foot placement. Model-based foothold planners provide precise foothold selection but rely heavily on explicit model assumptions. Recent attention-based map encoding (AME) studies show that end-to-end reinforcement learning (RL) can learn implicit foothold guidance. However, the computational cost of dense AME encoding grows with map resolution, limiting its scalability to fine-grained sparse terrain. We propose DELTA, a Deformable Elevation-Based Local Terrain Attention encoder. DELTA predicts state-conditioned sampling locations, forms terrain evidence tokens from adaptive local elevation patches, and attends only to a fixed-size token set. With fixed sampling and patch settings, DELTA's encoder cost is independent of map resolution. Experiments show that DELTA achieves final traversal performance comparable to AME at the standard resolution while improving learning efficiency. This fixed encoder cost enables the use of higher-resolution terrain maps, improving traversal on fine-grained sparse terrain. DELTA also demonstrates strong generalization to unseen mixed evaluation courses composed of continuous and discrete terrain elements. Beyond simulation, DELTA demonstrates successful sim-to-real transfer on RAIBO2. Analysis of the learned sampling offsets and attention weights shows that DELTA samples steppable regions and attends to terrain evidence relevant to future touchdowns without foothold labels or attention supervision.

Sanghyun Park, Moonkyu Jung, Jemin Hwangbo

Robotics and Artificial Intelligence Lab, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon 34141, Republic of Korea

Terrain-Aware Robot Locomotion Robot Locomotion Quadruped Locomotion Quadruped Robot Navigation RL for Legged Locomotion Deformable Attention

Jul 16, 2026

Learning Agile Navigation in Crowded Environments for Quadruped Robots

Navigating dynamic and crowded environments presents significant challenges for quadruped robots due to severe sensor occlusion and unpredictable human motion. Existing approaches face a trade-off: model-based methods, such as Velocity Obstacles (VO), theoretically guarantee safety but rely on accurate obstacle motion estimates that often fail in dense crowds, while end-to-end learning methods offer robustness but lack motion prediction capability of obstacles, leading to collisions or conservative behaviors. To solve this, we propose VOP-Nav, a novel navigation system that combines the geometric safety of VO with the agile adaptability of end-to-end learning. Using only local onboard observations, our system avoids explicit obstacle detection and tracking pipelines. The VOP-Net processes multi-frame LiDAR data to implicitly encode dynamic constraints and predict a safe velocity region derived from Velocity Obstacle theory. Importantly, the VO predictions serve a dual role: they are used as input to the navigation policy during inference and as a reward signal during training to encourage safe motion. Evaluations in Isaac Gym demonstrate that VOP-Nav achieves higher success rates than all baselines while balancing locomotion speed and collision avoidance. Real-world deployment on a Unitree Go2 quadruped robot further validates the system's robustness and efficiency in complex indoor and outdoor dynamic environments.

Shuyu Wu, Zeyu Liu, Tianbao Zhang +6

Shanghai Jiao Tong University, Shanghai 200240, China · State Key Laboratory of High-end Heavy-load Robots, Midea Group, Foshan 528300, China

Robot Policy Learning Robot Navigation Dynamic Obstacle Avoidance Quadruped Robot Navigation

Jul 13, 2026

Stop to Decide: Latency-Aware Proprioceptive Navigation Primitives for Mapping-Free Quadruped Inspection

Compute-constrained quadrupeds often run their navigation loop far below the controller's design rate: sharing the onboard Jetson Orin with the vision pipeline slows our stair loop to about 15 Hz. This latency breaks a standard proprioceptive pattern: declaring stair-summit arrival from the body-pitch signal while still climbing. On a stepped platform whose 50 cm top is shorter than the robot (Unitree Go2, about 75 cm), in-motion detection overshoots the top edge with probability rising with the per-period advance v/f (the slowest about 15 Hz cell partly diluted by a separate non-arrival mode), whereas a climb-settle cadence holds overshoot near zero at every loop rate (pooled 22/45 vs 1/45 over about 30/20/15 Hz; Fisher p about 2.4e-7; 7/15 vs 0/15 at the deployed about 15 Hz). A logistic dose-response model in v/f captures the failure; a pre-specified 40 Hz out-of-sample test favours the protocol-clean fit (33% observed vs 43%/22% predicted), giving a deployment rule (critical loop rate about 19 Hz at 0.30 m/s). The detector sits in a fully onboard, mapping-free and learning-free stack: built-in inertial measurement unit, four foot-force channels, three 1-D ranges, one line camera, chaining line-following, a three-segment maneuver for 90-degree corners in a 55 cm corridor (20/20 contact-free vs 14/20 with 12 wall contacts for in-place yaw; exit-heading error 1.56 degrees vs 5.64 degrees), and stair traversal, completing the inspection course in 18/20 trials (90%). Results are from a single course geometry, platform, and operator.

Hanting Suo, Haonan Yan, Liang Wang +1

School of Instrument Science and Engineering, Southeast University, Nanjing, China. · School of Electrical Engineering, Southeast University, Nanjing, China. · School of Mechanical Engineering, Southeast University, Nanjing, China.

Robotic Control Robot Navigation Terrain-Aware Robot Locomotion Quadruped Locomotion Quadruped Robot Navigation

Jun 17, 2026

SCAN-Planner: Spatial Collision-Aware Local Planning for Route-Guided Long-Range Quadruped Navigation

Quadruped robots are increasingly expected to navigate through narrow passages, cluttered indoor scenes, and large-scale 3D unstructured environments. Existing local planners commonly approximate the robot using isotropic geometric inflation or rely on planar and elevation-map representations, leading to conservative motion in tight spaces and limited reasoning about overhanging structures. This letter presents SCAN-Planner, a spatial collision-aware local planning framework for long-range quadruped navigation. A yaw-aware twin-cylinder footprint is used to model the elongated robot body, enabling whole-body collision evaluation through sparse queries in an inflated 3D occupancy map. We further introduce a projected A* search that generates collision-free guidance on an interpolated ground-following surface, with z-gradient suppression to avoid obstacles horizontally while maintaining vertical stability. For large-scale deployment, a robot-centric sliding map with boundary fallback provides high-resolution local collision checking and recovery from local dead ends. Simulation and real-world experiments demonstrate that SCAN-Planner generates safe, smooth, and efficient trajectories in dense clutter, 3D unstructured scenes, stair traversal, and long-range navigation tasks.

Han Zheng, Zhe Chen, Yiwen Fu +2

Shanghai Jiao Tong University, Shanghai, China.

Collision Avoidance Robot Navigation Safe Robot Navigation Quadruped Robot Navigation Robot Motion Planning

May 13, 2026

Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels

Quadruped robots demonstrate exceptional potential for navigating complex terrain in critical applications such as search and rescue missions and infrastructure inspection However autonomous traversal of confined 3D environments including tunnels caves and collapsed structures remains a significant challenge Existing methods often struggle with rigid gait patterns limited adaptability to diverse geometries and reliance on oversimplified environmental assumptions This paper introduces a Reinforcement Learning RL framework that combines procedural environment generation with policy distillation to enable robust locomotion across various tunnel configurations Our approach leverages a teacher student training paradigm where specialized expert policies trained on procedurally generated tunnel geometries transfer their knowledge to a unified student policy This strategy eliminates the need for complex reward shaping in end-to-end RL training simplifying the process by breaking down complicated tasks into smaller more manageable components that are easier for the robot to learn By synthesizing diverse tunnel structures during training and distilling navigation strategies into a generalizable policy our method achieves consistent traversal across complex spatial constraints where conventional approaches fail We demonstrate through both simulation and real world experiments that our method enables quadruped robots to successfully traverse challenging confined tunnel environments

Amir Hossain Raj, Dibyendu Das, Xuesu Xiao

Department of Computer Science, George Mason University

Legged Robotics RL for Legged Locomotion Quadruped Locomotion Quadruped Robot Navigation

Apr 29, 2026

HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments

Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and posture adaptation to traverse narrow, height-constrained spaces. Conventional approaches employ a sequential mapping-planning pipeline but suffer from accumulated perception errors and high computational overhead, restricting their applicability on resource-constrained platforms. To address these challenges, we propose Hierarchical Posture-Adaptive Navigation (HiPAN), a framework that operates directly on onboard depth images at deployment. HiPAN adopts a hierarchical design: a high-level policy generates strategic navigation commands (planar velocity and body posture), which are executed by a low-level, posture-adaptive locomotion controller. To mitigate myopic behaviors and facilitate long-horizon navigation, we introduce Path-Guided Curriculum Learning, which progressively extends the navigation horizon from reactive obstacle avoidance to strategic navigation. In simulation, HiPAN achieves higher navigation success rates and greater path efficiency than classical reactive planners and end-to-end baselines, while real-world experiments further validate its applicability across diverse, unstructured 3D environments.

Jeil Jeong, Minsung Yoon, Seokryun Choi +3

School of Computing, KAIST, South Korea.

Robot Navigation Quadruped Robot Navigation Vision-Based Robot Control Quadruped Locomotion

Mar 13, 2026

SmoothTurn: Learning to Turn Smoothly for Agile Navigation with Quadrupedal Robots

Quadrupedal robots show great potential for valuable real-world applications such as fire rescue and industrial inspection. Such applications often require urgency and the ability to navigate agilely, which in turn demands the capability to change directions smoothly while running in high speed. Existing approaches for agile navigation typically learn a single-goal reaching policy by encouraging the robot to stay at the target position after reaching there. As a result, when the policy is used to reach sequential goals that require changing directions, it cannot anticipate upcoming maneuvers or maintain momentum across the switch of goals, thereby preventing the robot from fully exploiting its agility potential. In this work, we formulate the task as sequential local navigation, extending the single-goal-conditioned local navigation formulation in prior work. We then introduce SmoothTurn, a learning-based control framework that learns to turn smoothly while running rapidly for agile sequential local navigation. The framework adopts a novel sequential goal-reaching reward, an expanded observation space with a lookahead window for future goals, and an automatic goal curriculum that progressively expands the difficulty of sampled goal sequences based on the goal-reaching performance. The trained policy can be directly deployed on real quadrupedal robots with onboard sensors and computation. Both simulation and real-world empirical results show that SmoothTurn learns an agile locomotion policy that performs smooth turning across goals, with emergent behaviors such as controlling momentum when switching goals, facing towards the future goal in advance, and planning efficient paths.

Zunzhi You, Yunke Wang, Haolan Guo +1

School of Computer Science, University of Sydney

Robot Policy Learning Robot Navigation RL for Legged Locomotion Quadruped Locomotion Quadruped Robot Navigation

Mar 10, 2026

SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments

Efficiently learning safe and agile quadruped navigation in densely cluttered environments remains difficult: existing methods often lack safety and agility, or become conservative in complex scenes and require long training schedules. We propose SEA-Nav (Safe, Efficient, and Agile Navigation), a safe reinforcement learning framework for quadruped navigation in cluttered environments. A differentiable control barrier function (CBF) shield constrains the policy to produce safe velocity commands. An adaptive collision-state initialization mechanism increases the probability of learning from safety-critical near-collision experience. An action regularization term further suppresses infeasible commands for physical deployment. The policy converges after about one hour of training on a single RTX 4090 and transfers zero-shot to real-world cluttered scenes.

Shiyi Chen, Mingye Yang, Haiyi Liu +6

Tsinghua University, Beijing, China. · Imperial College London, London, UK.

Quadruped Robot Navigation Robot Navigation Safe Robot Navigation Safe RL Data-Efficient Robot Learning

Mar 6, 2026

Task-Level Decisions to Gait Level Control: A Hierarchical Policy Approach for Quadruped Navigation

Real-world quadruped navigation is constrained by a scale mismatch between high-level navigation decisions and low-level gait execution, as well as by instabilities under out-of-distribution environmental changes. Such variations challenge sim-to-real transfer and can trigger falls when policies lack explicit interfaces for adaptation. In this paper, we present a hierarchical policy architecture for quadrupedal navigation, termed Task-level Decision to Gait Control (TDGC). A low-level policy, trained with reinforcement learning in simulation, delivers gait-conditioned locomotion and maps task requirements to a compact set of controllable behavior parameters, enabling robust mode generation and smooth switching. A high-level policy makes task-centric decisions from sparse semantic or geometric terrain cues and translates them into low-level targets, forming a traceable decision pipeline without dense maps or high-resolution terrain reconstruction. Different from end-to-end approaches, our architecture provides explicit interfaces for deployment-time tuning, fault diagnosis, and policy refinement. We introduce a structured curriculum with performance-driven progression that expands environmental difficulty and disturbance ranges. Experiments show higher task success rates on mixed terrains and out-of-distribution tests.

Sijia Li, Haoyu Wang, Shenghai Yuan +2

Quadruped Robot Navigation Robot Navigation Quadruped Locomotion Terrain-Aware Robot Locomotion Hierarchical RL Sim-to-Real Transfer

Mar 4, 2026

Efficient Autonomous Navigation of a Quadruped Robot in Underground Mines on Edge Hardware

Embodied navigation in underground mines faces significant challenges, including narrow passages, uneven terrain, near-total darkness, GPS-denied conditions, and limited communication infrastructure. While recent learning-based approaches rely on GPU-accelerated inference and extensive training data, we present a fully autonomous navigation stack for a Boston Dynamics Spot quadruped robot that runs entirely on a low-power Intel NUC edge computer with no GPU and no network connectivity requirements. The system integrates LiDAR-inertial odometry, scan-matching localization against a prior map, terrain segmentation, and visibility-graph global planning with a velocity-regulated local path follower, achieving real-time perception-to-action at consistent control rates. After a single mapping pass of the environment, the system handles arbitrary goal locations within the known map without any environment-specific training or learned components. We validate the system through repeated field trials using four target locations of varying traversal difficulty in an experimental underground mine, accumulating over 700 m of fully autonomous traverse with a 100% success rate across all 20 trials (5 repetitions x 4 targets) and an overall Success weighted by Path Length (SPL) of 0.73 \pm 0.09.

Yixiang Gao, Kwame Awuah-Offei

Department of Mining and Explosive Engineering, Missouri University of Science and Technology, Rolla, USA

Quadruped Robot Navigation Robot Navigation Robotics Off-Road Robot Navigation

Feb 13, 2026

TRAIN: Terrain-Aware Interactive Navigation for Quadruped Robots via Reinforcement Learning

This study introduces TRAIN: TeRrain-Aware Interactive Navigation, a deep reinforcement learning (DRL)-based framework for quadrupedal navigation across unstructured terrain and human-populated environments. Conventional quadrupedal navigation typically separates motion planning from locomotion control, neglecting whole-body constraints and terrain awareness. On the other hand, end-to-end methods are more integrated but require high-frequency sensing, which is often noisy and computationally costly. In addition, most existing approaches assume static environments, limiting their use in human-populated settings. To address these limitations, we propose a two-stage training framework with three DRL pipelines. (1) TRAIN-Loco employs an asymmetric actor-critic (AC) model for quadrupedal locomotion, enabling agile traversal of uneven terrains without explicit terrain or contact observations. (2) TRAIN-Nav applies a symmetric AC framework for navigation with interactive pedestrians, directly mapping transformed LiDAR data to ego-agent actions under differential-drive kinematics. (3) A unified pipeline, TRAIN, integrates TRAIN-Loc and TRAIN-Nav, supporting terrain-aware quadrupedal navigation in uneven and interactive environments. Comprehensive benchmarks against quadrupedal locomotion and robot navigation baselines demonstrate the effectiveness of TRAIN. Hardware experiments further confirm its potential for sim-to-real transfer.

Wei Zhu, Irfan Tito Kurniawan, Ye Zhao +1

Department of Robotics, Graduate School of Engineering, Tohoku University, 980-8579, Sendai, Japan. · Laboratory for Intelligent Decision and Autonomous Robots, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA.

Quadruped Robot Navigation Terrain-Aware Robot Locomotion RL for Legged Locomotion Social Navigation